Portfolio_3
Ben Hardin
2023-02-28
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0 ✔ purrr 0.3.5
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.5.0
## ✔ readr 2.1.3 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(jsonlite)##
## Attaching package: 'jsonlite'
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## The following object is masked from 'package:purrr':
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## flattenspotify <- "StreamingHistory0.JSON" %>%
fromJSON() %>%
as_tibble() %>%
glimpse()## Rows: 7,017
## Columns: 4
## $ endTime <chr> "2022-02-24 00:05", "2022-02-24 02:06", "2022-02-24 02:17",…
## $ artistName <chr> "Two Psychologists Four Beers", "Kamasi Washington", "Super…
## $ trackName <chr> "Episode 81: Against Retribution", "Clair de Lune", "Vive L…
## $ msPlayed <int> 2164222, 1270, 326, 667733, 828000, 963, 2152, 7530, 254053…spotify %>%
count(artistName) %>%
arrange(desc(n))## # A tibble: 1,009 × 2
## artistName n
## <chr> <int>
## 1 The Mountain Goats 710
## 2 Very Bad Wizards 526
## 3 Unknown Artist 145
## 4 The Beatles 140
## 5 Japanese Breakfast 117
## 6 Phoebe Bridgers 102
## 7 Wednesday 99
## 8 CAKE 94
## 9 Decoding the Gurus 89
## 10 St. Vincent 81
## # … with 999 more rowsspotify <- spotify %>%
filter(!artistName %in% c("Very Bad Wizards", "Unknown Artist", "Decoding the Gurus", "Taskmaster The Podcast", "Two Psychologists Four Beers", "Better Call Saul Insider Podcast", "Off Menu with Ed Gamble and James Acaster"))
spotify <- spotify %>%
group_by(artistName) %>%
mutate(plays = n())spotify_top <- spotify %>%
filter(plays >= 40)
spotify_top %>%
count(artistName) %>%
arrange(desc(n))## # A tibble: 21 × 2
## # Groups: artistName [21]
## artistName n
## <chr> <int>
## 1 The Mountain Goats 710
## 2 The Beatles 140
## 3 Japanese Breakfast 117
## 4 Phoebe Bridgers 102
## 5 Wednesday 99
## 6 CAKE 94
## 7 St. Vincent 81
## 8 Sleater-Kinney 77
## 9 R.E.M. 70
## 10 AJJ 68
## # … with 11 more rowsggplot(spotify_top, aes(y = artistName, x = endTime, color = artistName))+
geom_point(position = "jitter")+
theme(axis.text.y = element_blank())
library(lubridate)## Loading required package: timechange##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
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## date, intersect, setdiff, unionspotify_top$month <- month(ymd_hm(spotify_top$endTime))
spotify_top <- spotify_top %>%
group_by(month, artistName) %>%
mutate(monthly_plays = n())
spotify_monthly <- aggregate(monthly_plays ~ month + artistName, data = spotify_top, FUN = mean)ggplot(spotify_monthly, aes(x = month, y = monthly_plays, color = artistName))+
geom_line()
spotify_top8 <- spotify_top %>%
filter(plays > 80)
spotify_monthly <- aggregate(monthly_plays ~ month + artistName + plays, data = spotify_top8, FUN = mean)ggplot(spotify_monthly, aes(x = month, y = monthly_plays, color = fct_reorder(artistName, plays, .desc = TRUE)))+
geom_line(linewidth = 1.3)
Make it better
spotify_monthly <- spotify_monthly %>%
mutate(month_name = month.name[spotify_monthly$month])
ggplot(spotify_monthly, aes(x = fct_reorder(month_name, month), y = monthly_plays, group = fct_reorder(artistName, plays, .desc = TRUE), color = fct_reorder(artistName, plays)))+
geom_line(lineend = "round", aes(linewidth = fct_reorder(artistName, plays)))+
scale_color_viridis_d()+
theme_bw()+
labs(title = "Listening Trends for Ben's top artists (2022)",
x = "Month",
y = "Plays",
color = "Artist",
linewidth = "Artist")## Warning: Using linewidth for a discrete variable is not advised.
spotify_monthly <- spotify_monthly %>%
mutate(month_name = month.name[spotify_monthly$month])
ggplot(spotify_monthly, aes(x = fct_reorder(month_name, month), y = monthly_plays, group = fct_reorder(artistName, plays, .desc = TRUE), color = fct_reorder(artistName, plays)))+
geom_line(lineend = "round",
aes(linewidth = fct_reorder(artistName, plays)))+
guides(color = guide_legend(reverse = TRUE), linewidth = guide_legend(reverse = TRUE))+
scale_color_viridis_d(option = "F", direction = 1)+
theme_classic()+
theme(
legend.position = c(.95, .95),
legend.justification = c("right", "top"),
legend.box.just = "right",
legend.margin = margin(6, 6, 6, 6))+
labs(title = "Listening Trends for Ben's top artists (2022)",
x = "Month",
y = "Plays",
color = "Ben's top artists",
linewidth = "Ben's top artists")## Warning: Using linewidth for a discrete variable is not advised.
Mapping the geography of my music
artist_plays <- spotify %>%
count(artistName) %>%
filter(n > 11) %>%
filter(!artistName %in% c("You're Wrong About", "My Brother, My Brother And Me"))[after all the making functions and writing scripts we just did]
artist_towns <- read.csv("data/artist_towns.csv")
origin_coords <- read.csv("data/origin_coords.csv")
artist_map <- inner_join(artist_plays, artist_towns, by = "artistName")
artist_map <- inner_join(artist_map, origin_coords, by = "origin") %>%
filter(!is.na(long))artist_map$long.dec <- dms2dec(artist_map$long)
artist_map$lat.dec <- dms2dec(artist_map$lat)
#credit:https://www.r-bloggers.com/2022/02/degree-minute-second-to-decimal-coordinates/library(maps)##
## Attaching package: 'maps'## The following object is masked from 'package:purrr':
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## mapworld <- map_data("world")
ggplot(artist_map, aes(x = long.dec, y = lat.dec))+
geom_point()+
geom_polygon(data = world, aes(x = long, y = lat))
ggplot()+
geom_polygon(data = world, aes(x = long, y = lat, group = group), fill = "grey90")+
geom_point(data = artist_map, aes(x = long.dec, y = (lat.dec), size = n, label = artistName), color = "skyblue3", alpha = .7)+
theme_void()+
theme(legend.position = "none")## Warning in geom_point(data = artist_map, aes(x = long.dec, y = (lat.dec), :
## Ignoring unknown aesthetics: label
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
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## last_plot## The following object is masked from 'package:stats':
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## filter## The following object is masked from 'package:graphics':
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## layoutmusic_map <-
ggplot()+
geom_polygon(data = world, aes(x = long, y = lat, group = group), fill = "grey90")+
geom_point(data = artist_map, aes(x = long.dec, y = (lat.dec), size = n, label = artistName), color = "skyblue3", alpha = .7)+
theme_void()+
theme(legend.position = "none")## Warning in geom_point(data = artist_map, aes(x = long.dec, y = (lat.dec), :
## Ignoring unknown aesthetics: labelggplotly(music_map, tooltip = "label")